Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 363-369.doi: 10.11896/jsjkx.210500044

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Multi-tree Network Multi-crop Early Disease Recognition Method Based on Improved Attention Mechanism

GAO Rong-hua1,2, BAI Qiang1,2,3, WANG Rong1,2,3, WU Hua-rui1,2, SUN Xiang1,2   

  1. 1 Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China
    2 National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China
    3 College of Information Engineering,Northwest A＆F University, Xianyang,Shaanxi 712100,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:GAO Rong-hua,born in 1977,Ph.D candidate,associate professor,is a member of China Computer Federation.Her main research interests include big data analysis and intelligent decision-ma-king.
    BAI Qiang,born in 1997,postgraduate.His main research interests include computer vision and object detection.
  • Supported by:
    National Natural Science Foundation of China(61771058) and Beijing Municipal Science and Technology Project(Z191100004019007).

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Abstract: In the early stage of crop infection,timely acquisition of crop disease information,identification of the cause and severity of disease,and the right remedy,can prevent and control the decline in crop yield caused by the spread of the disease in time.In view of the low accuracy of traditional deep learning network for early crop disease recognition,based on the difference in information contained in each channel of the disease feature image and the characteristics of multilayer prceptron(MLP) that can approximate any function,a multi-tree network crop early disease identification method based on improved attention mechanism is proposed.It combines the attention mechanism with residual network to recalibrate disease features(SMLP_Res).At the same time,combined with the multi-tree structure,the SMLP_ResNet network with high feature extraction ability is expanded,and the constructed multi-tree SMLP_ResNet network model can simplify the task of early disease recognition of multiple crops and effectively extract early disease features.In experiments,Plant Village and AI Challenger 2018 are used to train18-layer model ResNet,SE_ResNet,SMLP_ResNet,as well as the multi-tree structure model with the same structure,to verify the influence of SMLP_Res and multi-tree structure on crop disease recognition models.Experimental analysis shows that,the disease recognition accuracy of the three network models on Plant Village dataset with obvious disease features is more than 99%,but their accuracy on the early disease data set AI Challenger 2018 is not more than 87%.SMLP_ResNet has sufficient feature extraction of crop disease data due to the addition of SMLP_Res module,and the detection results are better.The three early disease recognition models of the multi-tree structure significantly improves the recognition accuracy on AI Challenger 2018 dataset.The multi-tree SMLP_ResNe has better performance than the other two models,and the early disease recognition accuracy of cherry is 99.13%,the detection result is the best.The proposed multi-tree SMLP_ResNet crop early disease recognition model can simplify the recognition task,suppress noise transmission,and achieve a higher accuracy rate.

Key words: Attention mechanism, Early disease recognition, Loss function, Multi-tree, Residual network

CLC Number: 

  • S431
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